Lead Data Engineer, AI & Modernization
About The Position
The Lead Data Engineer leads data-engineering work on migration engagements, building pipelines and data products that supply AI systems. The role applies evidence-extraction techniques—including dependency analysis and git-history mining—to understand the source estate before trusting mappings. It is part of Accenture’s Digital Core Reinvention practice, which engineers data foundations for AI systems based on evidence about what is actually present in the estate. The position is suited to practitioners with data engineering or platform engineering experience and hands-on work with AI/LLM-related systems. Travel may be required, varying from 0% to 100% depending on business need and client requirements.
Requirements
- At least 5 years of hands-on data engineering or data platform engineering experience, including at least 1–2 years specifically on AI/LLM-related data work.
- At least 4 years of experience working in agile engineering or consulting teams.
- At least 3 years using Python and SQL at production scale, plus experience with at least one cloud data platform.
- Working fluency with data lineage and data observability.
- At least 1 year designing data supply for AI systems, such as vector stores, embedding pipelines, or agent-facing data products.
- Bachelor's degree or equivalent (minimum 12 years) work experience; applicants with an associate's degree must have at least 6 years of work experience.
- Applicants for employment in the United States must have work authorization that does not now or in the future require visa sponsorship.
Nice To Haves
- Streaming and CDC experience with Kafka, Flink, or equivalent.
- Exposure to code-analysis or archaeology tooling.
- Experience designing agentic data architecture to supply data to multiple cooperating agents.
Responsibilities
- Lead data-engineering work on migration engagements.
- Build data pipelines and data products that feed AI systems.
- Apply evidence-extraction techniques, including dependency analysis and git-history mining, to analyze source estates before trusting mappings.
- Design data supply for AI systems, including vector stores, embedding pipelines, or agent-facing data products.
- Use data lineage and data observability to identify data drift before it surfaces as AI drift.
Benefits
- Medical coverage
- Dental coverage
- Vision coverage
- Life insurance
- Long-term disability coverage
- 401(k) plan
- Bonus opportunities
- Paid holidays
- Paid time off
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What This Job Offers
Job Type
Full-time
Career Level
Senior
